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Research Highlights: Singapore Researchers Look to Intel Neuromorphic Computing to Help Enable Robots That ‘Feel’

Today, two researchers from the National University of Singapore (NUS), who are members of the Intel Neuromorphic Research Community (INRC), presented new findings demonstrating the promise of event-based vision and touch sensing in combination with Intel’s neuromorphic processing for robotics. The work highlights how bringing a sense of touch to robotics can significantly improve capabilities and functionality compared to today’s visual-only systems and how neuromorphic processors can outperform traditional architectures in processing such sensory data.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – June 2020

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

The insideBIGDATA IMPACT 50 List for Q3 2020

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

Research Highlights: ExBERT

In the insideBIGDATA Research Highlights column we take a look at new and upcoming results from the research community for data science, machine learning, AI and deep learning. Our readers need to get a glimpse for technology coming down the pipeline that will make their efforts more strategic and competitive. In this installment we review a new paper: EXBERT: A Visual Analysis Tool to Explore Learned Representations in Transformers Models by researchers from the MIT-IBM Watson AI Lab and Harvard.

Special Report: The State of AI and Machine Learning

Appen Limited, a leading provider of high-quality training data for organizations that build effective AI systems at scale, released its annual State of AI Report for 2020. The report highlights increasing C-suite involvement and investment in enterprise AI projects as well as data being a key challenge as AI models get more frequent updates in production. The report also reveals the recent acceleration of AI strategies in the wake of the COVID-19 pandemic.

Research Highlights: YOLO Revisited

In the insideBIGDATA Research Highlights column we take a look at new and upcoming results from the research community for data science, machine learning, AI and deep learning. Our readers need to get a glimpse for technology coming down the pipeline that will make their efforts more strategic and competitive. In this installment we review a new update of the highly-acclaimed real-time object detector “You Only Look Once” or YOLO algorithm that is more accurate than ever.

Three Ways Data Scientists are Fighting COVID-19

Data scientists in academia, non-profits, and the government have come together to track and respond to the economic & humanitarian impacts of the coronavirus. Put together by our friends over at SafeGraph, here are three ways data scientists are fighting COVID-19.

How to Produce Cleaner Data for Robust Pricing

In this contributed article by MIT Sloan School of Management Prof. Negin Golrezaei, found that there are ways to limit price manipulation. The key is the pricing algorithm. Instead of using bids to directly set prices, a prominent group of researchers designed an algorithm that uses censored bids– in this case a binary signal – to indicate whether the buyer wins in the prior auction or not.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – May 2020

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

Research Highlights: MIDAS – Real-time Anomaly/Fake News/Intrusion Detection

In the insideBIGDATA Research Highlights column we take a look at new and upcoming results from the research community for data science, machine learning, AI and deep learning. Our readers need to get a glimpse for technology coming down the pipeline that will make their efforts more strategic and competitive. In this installment we review MIDAS – Real-time Anomaly/Fake News/Intrusion Detection developed by Ph.D. candidate Siddharth Bhatia and his team at the National University of Singapore.